Search Results (353)

Food-safety systems increasingly need to manage large detection files and externally generated analytical results across multiple organizations while linking these records to batch-level control actions. This study proposes a blockchain-based dual-track mechanism for trusted circulation of food-safety detection data and batch-level risk control, using aflatoxin B1 (AFB1) as the empirical case. High-dimensional files are stored in InterPlanetary File System (IPFS) and anchored on-chain by content identifiers (CIDs); three authorized oracles use two-of-three matching of detection values and evidence hashes before contract-based state determination. A stage–device–operation inverted index identifies associated batches, while signed second-track confirmations drive GREEN/YELLOW/RED state transitions. Experiments on a four-node Quorum Byzantine Fault Tolerance (QBFT) network used 57 independent HyperPistachio samples with 86.625-mebibyte (MiB) band-interleaved-by-line (BIL) files. Real-file access was successfully completed, single-oracle failures were tolerated when two consistent oracle reports remained, and associated-batch query latency increased only from 13.06 to 16.78 ms as fanout rose from 1 to 40. A 115.15 min sustained run maintained consistent states across all four nodes. The study manages externally supplied AFB1 results rather than evaluating analytical AFB1 detection accuracy, and its experimental validation is limited to the AFB1 case. Full article

Federated learning (FL) enables collaborative model training without centralizing raw training records, but it does not inherently provide verifiable model provenance, enforceable fairness policies, or auditable control over aggregation. This paper presents FairAI, a blockchain- and IPFS-enabled framework that treats each local model as a governed artifact linked to performance and group-fairness metrics, content identifiers, manifests, Groth16 evidence, and smart-contract decisions. Only models approved on-chain and subsequently retrieved and validated through their registered CIDs are eligible for aggregation. The primary real-data evaluation used the Adult and COMPAS datasets under IID and joint label/protected-group non-IID partitions, with ten paired seeds comparing standard FedAvg, post hoc fairness assessment, a pre-aggregation fairness policy gate, and FairFed. Under heterogeneous Adult data, the policy gate reduced the demographic-parity gap from 0.0273 to 0.0127, while accuracy decreased from 0.7740 to 0.7629. Under heterogeneous COMPAS data, the equalized-odds gap decreased from 0.2262 to 0.1226, while accuracy decreased from 0.6495 to 0.5809; the paired accuracy and equalized odds differences remained significant after Holm correction, with adjusted p-values of 0.0318 and 0.0491, respectively. Additional bounded experiments evaluated a small multilayer perceptron, policy threshold sensitivity, logical-client scaling, poisoning, coordinate-wise median aggregation, two native Kubo/IPFS peers, V2 Groth16 verification, and smart-contract overhead. Thirty valid V2 proofs were accepted, six inconsistent cases were rejected, and direct Solidity verification consumed 348,811 gas per measured transaction. A full-path false-metric experiment showed that the proof verifies threshold compliance and artifact binding for supplied values, but does not establish their correct derivation from private data. When an approved artifact became unavailable, FairAI cancelled the round before aggregation and published no global model. Full article

Background/Objectives: Cyclin-dependent kinase 2 (CDK2) is a key regulator of cell-cycle progression and a potential anticancer target. This study aimed to identify alkaloid-derived CDK2 ligands using an integrated computational workflow and to obtain preliminary evidence of their effects on cancer-cell viability. Methods: Molecular docking with mVina and fast pulling of ligand (FPL) simulations were benchmarked using 20 experimentally characterized CDK2 inhibitors. A library of 2692 PubChem-derived alkaloids was screened, followed by ADMET evaluation, 100 ns molecular dynamics simulations, and FPL-based relative-affinity re-ranking. The three prioritized compounds were evaluated in HepG2 and HGC-27 cells using an MTT assay after 48 h of exposure. Results: Docking and FPL showed correlations with experimental affinity data of R_Dock = 0.549 ± 0.180 and R_W = −0.676 ± 0.119, respectively. CID 636885, CID 46184320, and CID 101691758 were prioritized for detailed evaluation. All three compounds reduced cell viability, with lower IC₅₀ values observed in HepG2 cells than in HGC-27 cells. CID 101691758 exhibited the highest growth-inhibitory activity among the tested compounds, with IC₅₀ values of 15.37 ± 0.46 µg mL⁻¹ in HepG2 cells and 52.64 ± 1.33 µg mL⁻¹ in HGC-27 cells. Conclusions: The workflow identified three preliminary alkaloid hits, with CID 101691758 showing the most favorable combined computational and cell-viability profile. However, the MTT assay does not establish direct CDK2 inhibition or kinase selectivity. Biochemical CDK2 inhibition, target-engagement, and kinase-panel studies are therefore required. Full article

Background: Ankylosing Spondylitis (AS) and inflammatory bowel disease (IBD) exhibit substantial pathophysiological overlap, including dysregulated innate immunity, barrier dysfunction, and Th17-mediated inflammation. However, whether Na⁺ overload-related genes (NRGs) exhibit shared transcriptional alterations in IBD and AS remains unclear. This study aimed to characterize the expression patterns of NRGs across IBD and AS and to identify candidate genes associated with both diseases. Methods: Differential expression analysis was performed to identify differentially expressed NRGs (DE-NRGs) in diseased tissues relative to normal tissues. Shared DE-NRGs between IBD and AS were screened and defined as common differentially expressed NRGs (Co-DE-NRGs). We then analyzed the correlations of these Co-DE-NRGs and explored their relationships with immune cell infiltration in target tissues. Four machine learning algorithms were applied to screen key NRGs associated with both IBD and AS. Potential therapeutic agents targeting these core biomarkers were predicted using drug–gene interaction databases, and molecular docking was conducted for further validation. Results: A total of 32 shared Co-DE-NRGs were identified for IBD and AS, with nine key regulatory NRGs recognized: CALR, CD63, CYBA, DYSF, HYOU1, IL1B, JAK1, MMP9, and STAT3. Exploratory MR analysis identified disease-specific associations between genetically predicted expression of NRGs and CD, UC, and AS. Genetically predicted STAT3 expression showed positive associations with CD and UC but an inverse association with AS and therefore did not represent a consistent risk factor across the three diseases. Furthermore, transcriptome-based drug-response analysis identified four candidate agents shared between AS and at least one IBD dataset: ciclosporin, BCL-LZH-4, BRD-K79669418, and CID-5951923. Exploratory molecular docking generated STAT3 binding poses for BCL-LZH-4 and CID-5951923, with DOCK Grid Scores of −35.321896 and −28.361128, respectively. CID-5951923 was selected for representative visualization of its predicted interaction with STAT3. Single-cell RNA-sequencing analysis identified tissue- and cell-type-specific STAT3 mRNA expression patterns in the analyzed IBD colonic and AS peripheral-blood datasets, with monocytes representing a major cell population exhibiting detected STAT3 expression in the AS dataset. Conclusions: These findings identify shared NRG-related transcriptional alterations in IBD and AS, with STAT3 emerging as a candidate gene associated with both diseases. Further experimental studies are required to determine whether these alterations reflect the involvement of NECSO and to evaluate their potential diagnostic or therapeutic relevance. Full article

Endophytic bacteria from medicinal plants are increasingly recognised as sources of antimicrobial metabolites. However, the endophytic bacterial community of Mitrephora heyneana remains poorly explored. In the present study, endophytic bacteria were isolated from the leaves of M. heyneana, collected from the Western Ghats of Tamil Nadu, India. Among seven isolates, the plant endophyte strain from leaf 6th strain (PEL6) was identified as Paenibacillus lactis through 16S rRNA gene sequencing. The ethyl acetate extract of PEL6 (EAE-PEL6) was subjected to gas chromatography–mass spectrometry (GC-MS) analysis, which putatively identified 32 metabolites based on GC-MS library matching, including pyrrolo [1,2-a] pyrazine-1,4-dione derivatives and triazole compounds as major constituents. The EAE-PEL6 demonstrated significant in vitro antibacterial activity against Staphylococcus aureus. In silico ADMET (absorption, distribution, metabolism, excretion, and toxicity), profiling predicted drug-likeness and pharmacokinetic properties of selected candidate compounds. Molecular docking suggested favourable binding of selected metabolites to S. aureus target proteins; however, these interactions require experimental validation. Density Functional Theory calculations indicated that CID 70504 had the lowest Highest Occupied Molecular Orbital (HOMO)–Lowest Unoccupied Molecular Orbital (LUMO) energy gap, reflecting higher electronic reactivity. Molecular electrostatic potential mapping further supported its enhanced binding propensity. Molecular dynamics simulations suggested structural stability, with Root Mean Square Deviation, Solvent Accessible Surface Area, radius of gyration, and hydrogen-bond analyses indicating stable interactions throughout the 100 ns trajectory. Overall, this study identifies P. lactis PEL6 as a promising endophytic source of anti-S. aureus metabolites and provides candidates for future purification, structural confirmation, and biological validation. Full article

High-stakes online assessment produces suspicious-event records, yet storage placement and burst admission remain insufficiently characterized. This article presents a Kubernetes-deployed provenance pipeline integrating Hyperledger Fabric, IPFS, SHA-256 verification, and RabbitMQ ingress. Media objects are retained in IPFS, while compact semantics, CIDs, and verification anchors are committed to Fabric. At 5 TPS, five 300-transaction runs completed under both direct large-payload and compact-anchor ledger conditions. At 20 TPS, compact anchoring of a pre-retained IPFS object sustained 19.40 ± 0.00 TPS with 0.22 ± 0.00 s mean ledger latency; direct large-payload submission achieved 14.20 ± 0.40 TPS with 31.62 ± 3.35 s latency. A write-load sweep identified 100 TPS as the transition point; higher loads were delay-dominated. Three 1000-VU PTS runs reduced mean response time from 4.76 ± 0.78 s to 2.40 ± 0.02 s, with similar failure rates. In three 30-message consumer-enabled runs, all 90 messages reached IPFS retention, Fabric commit, query visibility, and manual acknowledgement without retry, producer failure, or dead-letter outcome. Mean queue-drain and total completion times were 61.80 ± 0.71 s and 62.99 ± 0.15 s. The results isolate Fabric transaction-path payload cost and distinguish queue acceptance from downstream completion. Full article

Corporate carbon information disclosure is shaped not only by external regulatory and stakeholder pressures but also by firms’ internal strategic orientation and the prominence of environmental transformation in organizational reporting. Using 31,625 firm-year observations from Chinese A-share listed firms between 2016 and 2024, this study examines the association between green strategic orientation (GSO) and carbon information disclosure (CID) and evaluates reported green-transformation intensity (RGTI) as a statistical indirect pathway. GSO is measured using an author-constructed seven-item index based on firm-level environmental disclosure indicators. RGTI is constructed from database-provided annual-report frequencies of green-transformation-related terms and is assessed against contemporaneous and one-year-ahead green investment obtained from a separate CSMAR green-investment database. The empirical analysis employs firm and year fixed effects, firm-clustered standard errors, and a three-wave design using GSO at t − 2, RGTI at t − 1, and CID at t. The results show that GSO is positively associated with CID. A one-standard-deviation increase in GSO is associated with an approximately 4.7-percentage-point increase in the disclosure index, equivalent to about 16.5% of its sample mean. The three-wave analysis identifies a positive and statistically significant indirect association through RGTI, although its economic magnitude is modest, accounting for approximately 8.0% of the total association. The findings remain robust to alternative measures of GSO and CID, fractional-response estimation, industry-year fixed effects, controls for general reporting propensity, and one-year-ahead disclosure specifications. Industry-based estimates indicate a weaker GSO–CID association among heavy-polluting firms, although this pattern is not reproduced when pollution exposure is measured using firm-level carbon intensity. Overall, the findings link internal environmental strategy and reported organizational attention to corporate carbon transparency while indicating that most of the GSO–CID association operates through channels beyond the measured indirect pathway. Full article

The rapid emergence of multidrug-resistant microbial pathogens has created an urgent need for new antimicrobial agents. Marine organisms are a rich source of structurally diverse bioactive metabolites with therapeutic potential, and the sea cucumber H. scabra contains numerous secondary metabolites that may exhibit favorable predicted interactions with antimicrobial targets. This study aimed to identify potential antimicrobial metabolites from H. scabra using an integrated computational approach. Metabolites were identified by gas chromatography–mass spectrometry (GC–MS) and screened against selected bacterial and fungal target proteins using molecular docking. The top-ranked compounds were further evaluated for pharmacokinetic properties through ADME prediction, while the stability of the protein–ligand complexes was assessed using 100 ns molecular dynamics simulations and MM/GBSA binding free energy calculations. GC–MS analysis tentatively identified sixteen metabolites, among which CID 21820889 and CID 304 exhibited favorable predicted binding interactions with target proteins from Acinetobacter baumannii and Candida albicans, respectively. These compounds also demonstrated acceptable predicted pharmacokinetic properties, stable protein–ligand interactions throughout the simulations, and favorable MM/GBSA binding free energies, supporting the stability of the predicted complexes. Overall, MM/GBSA binding free energies ranged from −35.46 to −47.17 kcal/mol, with CID 441130 exhibiting the most favorable overall binding energy (−47.17 kcal/mol), while CID 21820889 also showed a favorable binding free energy (−44.85 kcal/mol). These findings identify several putatively identified, computationally prioritized metabolites from H. scabra as candidates for further investigation and demonstrate the utility of integrating chemical profiling with computational approaches for the early stage screening of marine natural products. Full article

Peptides are the most extensively studied bioactive compounds derived from food. They are analyzed using, e.g., in silico strategy. The BIOPEP-UWM database has become a standard tool in computer-aided peptide research. The aim of this study was to equip this database with a tool enabling the annotation and processing of peptide or protein sequences containing modified amino acid residues as well as other residues. The most recent section of the BIOPEP-UWM, i.e., the repository of amino acids and modifications, apart from 20 proteinogenic amino acids, annotates non-amino acid moieties or residues subjected to enzymatic or chemical modifications (phosphorylation, oxidation, hydroxylation, acylation, etc.). This part of BIOPEP-UWM provides the following information: Compound ID, name, symbol in a special code, InChIKey identifier, SMILES representation, number in the PubChem database (CID), formula, and IDs in other databases. The search options include ID in the repository, name, symbol in a biological code, InChIKey, PubChem Compound identifier (CID), and chemical formula. Peptide or protein sequences annotated using symbols from the repository are utilized by all applications available in the BIOPEP-UWM database. The database meets contemporary trends involving modifications of amino acid residues in the bioinformatic analysis of peptides and proteins. Full article

Biological aerosols represent a significant challenge for modern CBRN defense due to their potential for long-range dispersion and the need for rapid threat assessment. Current stand-off detection systems are effective in recognizing anomalous aerosol clouds but often lack the molecular specificity required for reliable agent identification. This review examines the role of mid-infrared (MIR) spectroscopy as an emerging approach for chemically resolved stand-off bioaerosol sensing. The physical principles of MIR detection are discussed, including molecular vibrational fingerprints, differential scattering (DISC), and circular intensity differential scattering (CIDS), together with their relationship to aerosol optical properties and Mie resonance effects. Existing and emerging sensing architectures are reviewed, ranging from operational CO₂ laser-based DISC systems to semiconductor-based platforms utilizing tunable differential absorption lidar (DIAL), Quantum Cascade Lasers (QCLs), and dual-comb spectroscopy. The analysis highlights the ability of MIR sensing to access biomolecular signatures associated with proteins, lipids, nucleic acids, and bacterial spores, while also addressing challenges related to atmospheric attenuation, biological variability, and signal interpretation. The reviewed literature indicates that MIR spectroscopy offers a promising pathway toward improved stand-off identification of hazardous bioaerosols, supporting early threat detection and enhanced situational awareness in applications including CBRN defense, critical infrastructure protection, environmental monitoring, public health surveillance, and emergency response. Full article

Objectives: This study aimed to identify and characterize macrophage-associated inflammatory regulatory signatures in ulcerative colitis (UC) by integrating bulk and single-cell transcriptomic data, and to explore their potential regulatory and pharmacological relevance. Methods: Two colonic bulk microarray datasets (GSE179285 and GSE87466) and one single-cell RNA-sequencing dataset (GSE231993) were analyzed. Differential expression analysis, area under the recovery curve-based single-cell gene-set scoring (AUCell) scoring, macrophage high-dimensional Weighted Gene Co-Expression Network Analysis (hdWGCNA), and three machine learning algorithms were combined to prioritize candidate genes. Their expression and diagnostic performance were externally validated. Macrophage trajectory analysis, cell–cell communication analysis, virtual perturbation, transcription factor activity inference, compound prediction, and molecular docking were further performed. Results: Single-cell preprocessing retained 30,737 high-quality cells, and macrophages exhibited relatively high innate immune cell barrier-related gene activity. Integrated screening identified CCL3, CCL4, JUNB, and FOS, whereas machine learning consensus retained CCL3, CCL4, and JUNB as the final signatures. These genes were consistently upregulated in UC, with validation area-under-the-curve values of 0.917, 0.958, and 0.888, respectively. Their expression varied along an inferred macrophage inflammatory state continuum, and UC showed remodeled macrophage-centered communication, including CXCL8–ACKR1 signaling. Virtual perturbation linked CCL3 and CCL4 to chemotaxis and lysosomal programs and JUNB to antigen processing and major histocompatibility complex (MHC) class II pathways; RFX5 was prioritized as a potential upstream regulator. CID11879209 was predicted as a shared candidate compound, with docking energies of −6.55, −6.31, and −4.50 kcal/mol for CCL3, CCL4, and JUNB, respectively. Conclusions: CCL3, CCL4, and JUNB constitute a macrophage-associated inflammatory signature connecting tissue-level UC dysregulation with macrophage state remodeling. These findings provide testable molecular and pharmacological hypotheses requiring further experimental and clinical validation. Full article

The application of Internet of Things-based ecosystems is growing rapidly. Cyber attacks are also increasing at a similar pace. Intrusion detection using deep learning is getting harder as these devices lack enough resources for a large Intrusion Detection System. A compact deep learning-based Intrusion Detection System for IoT, called CID, has been proposed to reduce computational complexity. The proposed CID framework uses MobileNet v1 as the main classification model, and the Binary Greylag Goose Optimization technique is used for feature selection to improve detection while minimizing processing time. On comparing the experimental results, it has been found that the proposed method works better than the baseline methods. Full article

Background/Objectives: Tumors are induced by overactivation of oncogenes and loss of tumor suppressor genes. Recently, a family of proteins containing CID (C-terminal domain (CTD)-interacting domain) domains, named CREPT/RPRD1B and p15RS/RPRD1A, has been identified to be involved in tumorigenesis through the regulation of the cell cycle. Interestingly, while p15RS was shown to inhibit cell proliferation, CREPT was demonstrated to promote tumorigenesis by accelerating tumor cell cycle progression. Methods: To decipher why these two proteins function oppositely, we aimed to reveal the disparities in their clinical outcomes and protein properties. Results: We observed that CREPT and p15RS are both highly expressed in tumors, but with opposite prognostic implications. We confirmed that CREPT promotes, but p15RS inhibits cell proliferation via regulation of Wnt/β-catenin signaling activation. The CID domain of CREPT differs from that of p15RS in conformation and charge distribution. CREPT exhibits a significantly stronger oligomerization capacity than p15RS, which is mediated by the CCT (coiled-coil terminus) domain. We demonstrated that the differences in both CID and CCT domains between CREPT and p15RS contribute to their opposite physiological functions. Conclusions: In conclusion, our results demonstrate that despite high primary sequence similarity, CREPT and p15RS exhibit distinctive biochemical properties. These differences ultimately explain their functional divergence in tumorigenesis and offer novel insights into CREPT-targeted drug design. Full article

This study presents a novel biosensor for non-invasive glucose detection in saliva using sol colloids of erbium phthalocyanine (ErPc) and polyvinyl acetate (PVAc). The sensors were manufactured by depositing thin films on glass substrates and characterized via optical transmission spectroscopy in the UV-Vis range. The detection signal was based on variations in the transmission spectra amplitude after glucose intake. Results showed that the transmission response effectively distinguished between three health conditions: a regular individual, an athlete, and a prediabetic patient. Specifically, the relative transmission increased significantly in the prediabetic subject compared to the healthy individuals, demonstrating the biosensor’s capability to track glucose fluctuations non-invasively. Full article

As the Industrial Internet of Things becomes increasingly interconnected with critical infrastructure, intrusion traffic exhibits characteristics such as high-dimensional redundancy, class imbalance, and temporal correlation, posing challenges for detection systems in terms of feature representation, model complexity control, and real-time performance. To address the aforementioned issues, this paper proposes an intrusion detection algorithm based on feature optimization and asynchronous advantage actor-critic learning (APA3CID). First, the raw dataset was preprocessed using methods such as label encoding and normalization. Feature selection was performed using the improved Whale Optimization Algorithm (WOA) to reduce data redundancy and eliminate irrelevant features. The samples were then serialized based on the order in which they were collected. Second, we model the detection process as a Markov decision process, use a sliding window to construct states that capture recent temporal features, and, building upon the Asynchronous Advantage Actor-Critic (A3C) framework, we incorporate an adaptive exploration mechanism to address the issues of insufficient exploration in the early training phase and unstable convergence in the later phase. Additionally, we introduce an asynchronous lag correction strategy that utilizes truncated importance weights to mitigate the bias caused by policy lag in asynchronous parallel training, thereby enhancing the stability and robustness of policy updates. Finally, experimental results show that on the X-IIoTID dataset, APA3CID achieves a 3.51% increase in detection rate and a 4.26% increase in F1-score compared to the traditional A3C algorithm. On the WUSTL-IIoT-2021 dataset, single-sample prediction takes as little as 11.56 microseconds, with Acc, DR, and F1-score all exceeding 90%. This outperforms comparison models such as LR, XGBoost, CNN, and the baseline A3C, meeting the requirements of industrial IoT scenarios for low false-negative rates and high real-time performance. Full article